NMR chemical shifts provide important local structural information for proteins. Consistent structure generation from NMR chemical shift data has become feasible for proteins with sizes of up to about 100-130 residues, and such structures are of a quality comparable to those obtained with the standard NMR protocol. In collaboration with Dr. David Baker and his group, we developed a chemical-shift-guided approach to successfully and accurately determine structures on the basis of chemical shifts, but in practice the approach was limited to relatively small proteins. New work focuses on extending this approach to allow incorporation of easily accessible experimental information and more extensively exploit the available database of previously solved structures. By means of an optimized neural network algorithm, SPARTA+, we are able to estimate chemical shifts for proteins of known structure. Using this program, we are able to assign approximate chemical shift values to crystallographically determined structures. Standard bioinformatics tools, that have been designed originally for searching for proteins of remotely homologous sequence, can be adapted to now search the database for sequences with homologous chemical shifts and thereby find protein chains of similar structure, without requiring any sequence homology. The approach is proving to be very robust, and is able to use existing algorithms to deal with gaps in the sequence when searching for structural homologs. The method increases in efficiency with the size of the protein and therefore represents an ideal complement to the CS-Rosetta approach developed earlier by us in collaboration with the Baker group. It does require, however, the presence of prior solved protein of a similar fold in the database and will not reach convergence if no good structural templates can be found. The approach is computationally rather demanding, and requires access to cluster computing.